A Hybrid Genetic Algorithm for Weapon Target Assignment Optimization

As a classic issue in military operation research, weapon target assignment (WTA) has been of interest to researchers for a long time. The purpose of WTA is to determine the best assignment scheme to gain the largest benefit while satisfying a number of constraints deriving from the capability of available platforms and munitions (or weapons), target characteristics, timings for operation and so on. In order to overcome the drawbacks of easily falling into premature convergence and local optimum of existing heuristic algorithms when they are employed to optimize WTA problems, this paper proposes a hybrid genetic algorithm (HGA) which combines an adaptive genetic algorithm (AGA) with an adaptive variable neighborhood search algorithm (AVNSA) to balance the exploration and exploitation ability. In the framework of HGA, AGA is used for wide scope search in the solution space to avoid premature convergence while AVNSA for local search to jump out of the local optimal space. The simulation result demonstrates the effectiveness and feasibility of the proposed HGA in small-scale WTA optimization problems and through a comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm in large-scale WTA optimization problems are revealed.